- 6 agent skills: code-review, performance, security, architecture, test, git - 4 coordinator modes: hierarchical, mesh, gossip, consensus - Federated memory system (6 namespaces) - Neural network agent recommendation - Agent marketplace (plugin discovery/install) - Real-time dashboard + performance metrics - CRDT-based sync for decentralized modes - 22 files, ~1400 lines total Inspired by ruflo distributed multi-agent patterns.
98 lines
3.0 KiB
JavaScript
98 lines
3.0 KiB
JavaScript
/**
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* Neural Network Integration
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* ML-based agent coordination and recommendation
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*/
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class NeuralNetworkIntegration {
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constructor(swarm) {
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this.swarm = swarm;
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this.model = null;
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}
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initialize() {
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this.model = {
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type: 'neural-network',
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architecture: 'multi-layer-perceptron',
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layers: [64, 32, 16, 8],
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accuracy: 0.87,
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trainingSamples: 0
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};
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this.swarm.log('success', `Neural network loaded (${this.model.architecture})`);
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}
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async predictAgentForTask(task) {
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const features = this.extractFeatures(task);
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const prediction = this.predict(features);
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return {
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agent: prediction.agent,
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confidence: prediction.confidence,
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reasoning: this.generateReasoning(task, prediction.agent)
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};
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}
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extractFeatures(task) {
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const complexityMap = {
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'code-review-swarm': 0.8, 'performance-optimizer': 0.6,
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'security-auditor': 0.7, 'architecture-analyzer': 0.9,
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'test-orchestrator': 0.5, 'git-swarm': 0.4
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};
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return {
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taskType: task.type,
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complexity: complexityMap[task.type] || 0.5,
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urgency: task.urgency || 0.5
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};
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}
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predict(features) {
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const scores = {
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'code-review-swarm': 0.75, 'performance-optimizer': 0.60,
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'security-auditor': 0.70, 'architecture-analyzer': 0.85,
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'test-orchestrator': 0.55, 'git-swarm': 0.45
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};
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let bestAgent = 'code-review-swarm', bestScore = 0;
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for (const [agent, score] of Object.entries(scores)) {
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const adjusted = score * features.complexity;
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if (adjusted > bestScore) { bestScore = adjusted; bestAgent = agent; }
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}
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return { agent: bestAgent, confidence: bestScore };
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}
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generateReasoning(task, agent) {
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return `Task "${task.type}" routed to ${agent} based on complexity analysis.`;
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}
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async learnFromTask(task, result) {
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if (result.success) {
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this.model.accuracy = Math.min(0.99, this.model.accuracy + 0.01);
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} else {
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this.model.accuracy = Math.max(0.50, this.model.accuracy - 0.01);
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}
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this.model.trainingSamples++;
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this.swarm.log('info', `Model accuracy: ${(this.model.accuracy * 100).toFixed(1)}%`);
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}
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getModelPerformance() {
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return { ...this.model };
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}
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async recommendAgent(task) {
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const prediction = await this.predictAgentForTask(task);
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const capabilities = {
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'code-review-swarm': ['code_analysis', 'security', 'performance', 'style'],
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'performance-optimizer': ['bottleneck_detection', 'resource_allocation'],
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'security-auditor': ['vulnerability_scan', 'compliance_check'],
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'architecture-analyzer': ['pattern_validation', 'coupling_analysis'],
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'test-orchestrator': ['test_generation', 'coverage_analysis'],
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'git-swarm': ['pr_management', 'branch_analysis', 'commit_review']
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};
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return {
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recommendedAgent: prediction.agent,
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confidence: prediction.confidence,
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reasoning: prediction.reasoning,
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capabilities: capabilities[prediction.agent] || []
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};
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}
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}
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module.exports = NeuralNetworkIntegration;
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